Method and systems for predicting solar energy production

ABSTRACT

A method of anticipating a short-term future electrical energy demand of an energy trader&#39;s customers includes calculating a short-term future net electrical energy demand required to meet the demand of customer facilities which have a solar energy generating system or systems in a geographic region. The method further includes determining a resulting difference, expressed as a shortfall or surplus, between the short-term future net electrical energy demand and an amount of electrical energy purchased in long-term contracts for the supply of the customer facilities, and bargaining a buying price or a selling price for energy in a short-term future or day-ahead market based on the shortfall or surplus. A method for hedging energy sales or purchases in a short-term future or day-ahead market includes determining an historical performance of a regional net energy forecasting methodology for a facility or facilities which have solar energy generating systems in a geographical region. The method further includes estimating a difference between the maximum cost of energy in a spot market and an energy trader&#39;s minimum price of energy for each hour bid in the short-term future or day-ahead market, determining a risk factor associated with energy sales or purchases from the historical performance and the estimated difference, and purchasing or selling options to buy energy at the previous day&#39;s day-ahead market price based on the determined risk factor.

RELATED APPLICATIONS

This application is a divisional application of U.S. application Ser.No. 10/922,253, filed Aug. 19, 2004 which claims the benefit of U.S.Provisional Application No. 60/523,074, filed on Nov. 18, 2003; U.S.Provisional Application No. 60/507,899, filed on Oct. 1, 2003; and U.S.Provisional Application No. 60/496,411, filed on Aug. 20, 2003, theentire teachings of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

Since the first practical demonstration of photovoltaic (PV) cells,devices used to convert sunlight directly into electrical energy, wasperformed in 1954, people have sought to employ the technology forterrestrial energy production on an ever-increasing scale. To enablethis and other solar technologies, such as solar thermal and solarthermal-electric systems, to perform optimally, evaluation methods andprocesses have been developed by scientists and researchers to map therelative concentration of solar radiation hitting the earth's surface indifferent geographic locations. This process of mapping the sun'sintensity is generally referred to as solar resource assessment.Traditional solar resource assessment can, in many ways, be likened toprospecting, albeit with the use of sophisticated instruments and highlyrigorous scientific methods and practices. Some of the products of thisassessment process are databases that catalog the regional intensity ofthe solar resource, on an hourly basis, over the course of many years.These databases are often displayed as maps with topographic-likedemarcation and shadings corresponding to the intensity of the averageannual solar resource. Because the solar radiation hitting the earth'ssurface is highly dependant upon the local meteorological conditions itcan vary from one day, week, month or year to the next. Therefore, thedatabases usually contain time periods large enough (perhaps as much asthirty years) to smooth the year-to-year variation and approximate thecharacteristic annual climate conditions of the site being assessed. Asingle representative year of assessment data for a site can besynthesized from an average of a larger multi-year database. One methodused to create such a representative year is to select the most“typical” individual months from a database of thirty years, and combinethem to form a typical meteorological year (TMY). The resulting one-yeardatabase of irradiance and weather data is predictive of the conditionsthat can be expected for a site over a period similar to the originalsampling period of thirty years.

Once these solar resource databases are compiled they can be used asinputs into software programs that simulate electrical energy productionfrom solar generating systems. These simulation programs use the solarresource data for a given location, in combination with the physicalparameters of a particular solar generating system, —such as systemsize, orientation angles, equipment types, electrical characteristics,shading obstructions, latitude, longitude, elevation, and otherfeatures, needed to characterize the solar generator—to estimate theuseful energy that can be expected from the system over the course of aperiod of time comparable to the original data-sampling period. Usingsuch a process, it is possible to estimate the useful energy that wouldbe produced by a solar generating system over its thirty-year lifetime.

Presently, solar energy contributes only a tiny fraction of theelectrical energy consumed within the electrical grid. In its presentform, the electrical grid, both in its physical structure and in itsmarket configuration, is not designed to incorporate a significantpercentage of its daily energy transactions from solar energy. Because,with present technology, large amounts of electrical energy cannot bestored cost effectively, the grid requires constant management tobalance production with demand. Electricity is a product that must beused as soon as it is produced. Because it is so essential and criticalto our society, reliability standards are extremely high, often referredto as being in the “high nines.” This standard indicates that it isexpected that service will be present and within specifications 99.999%of the time. For the engineers who must manage the dispatch ofgeneration, the reserves and the transmission constraints of the grid,grid-tied solar energy presents no problem as long as its contributionis a small percentage of the energy flowing through the system. Fortraders in deregulated wholesale energy markets who resell energy to endusers, and who must anticipate their customer's demand, solar energygeneration that is distributed amongst those customers, presents noproblem, again, so long as that generation makes up only a smallpercentage of the total demand.

SUMMARY OF THE INVENTION

The economics of electrical energy are changing, and as the cost ofenergy created from solar generation systems approaches that oftraditional generation technologies, solar energy will become moreprevalent on the utility grid. Solar irradiance is, on average, on anannual basis, periodic and predictable. However, on an hourly basis, onthe earth's surface, solar energy is fundamentally intermittent and,generally not considered to be predictable. If solar energy generationsystems rise to meet a significant fraction of the daytime energy demandof the electrical grid, whether on the customer's side or on the supplyside of the revenue meter, they will present a serious managementchallenge to the stability, security, and reliability of the grid unlesstheir production can be accurately predicted on the same hourly basis asthat used to dispatch traditional generating resources. These sameconsiderations apply to energy markets. If solar energy generationsystems become a major contributor to the total energy mix within anoperating territory, they would also be potentially disruptive to theassociated wholesale electrical energy market, unless their contributioncan be accurately predicted on an hourly basis. Embodiments of thepresent invention can be used to predict hourly solar energy systemproduction in the next hour, or up to the next week.

Traditional solar resource assessment methods collect long-termirradiance data associated with particular geographic sites, either bydirect measurement or by modeling from meteorological observations. Theraw data sets may span decades. These historical data are distilled intoa single representative year for each geographic site. The distilled,site specific, single year, “average” or “typical,” data sets can thenbe used as inputs to solar generator simulation programs. This processestimates the yearly electrical energy production that the specificsimulated system would yield, were it to run over a timeframe comparableto the original data collection time. The problem with using thisprocess as an estimating method is that the long-term, historical dataset is static. Every time a simulation is conducted, based upon alocation's historical meteorological and irradiance profile, incombination with the unique characteristics of a specific solargenerator system, a single solution—a one-year prediction of hourlyelectrical energy production—will result. The simulation will alwaysproduce the same solution for a specific system, at a specific site. Thesolution represents a long-term average, of sorts, for the geographiclocation and solar generator being simulated or modeled.

Embodiments of the present invention can use a prospective data set,rather than a static retrospective data set. Further, in accordance withthe present invention, a unique solution can be attained with everyiteration of the simulation (unless two one-week, hourly forecastsresult in exactly identical meteorological and irradiance conditions forthe entire forecast period) by using forecast meteorological data topredict a location's immediate short-term future irradiance profile.Furthermore, embodiments of the present invention can achieve week-aheadand hour-ahead hourly regional predictions of solar energy production bycombining state of the art one-week weather forecasting, irradiancemodeling technology, and solar energy generation simulation techniques.With this approach an electricity grid can function with a significantproportion of its generation capacity supplied by solar energy systemsdistributed throughout its network. Another aspect of the inventionprovides an enhancement to the forecasting tools grid operatorscurrently use to anticipate their load. Another aspect provides a methodfor energy traders to anticipate their near-term contractual obligationsand to adjust their position in the market. In addition, new tradableenergy products can be defined that provide a mechanism to manage thefinancial risk associated with the inherent intermittency of solarenergy.

Accordingly, there is provided a method of forecasting energy productionfor solar generating systems in a region by collecting meteorologicaldata for a given area of the region, estimating irradiance levels usingparameters collected from the meteorological data, and simulating solarenergy production using the collected meteorological data, estimatedirradiance levels, and physical characteristics of a solar generatingsystem in the given area of the region. The parameters can includeglobal horizontal (GH) irradiance, diffuse horizontal (DH) irradiance,and direct normal (DN) irradiance. The meteorological data can becollected on a week-ahead hourly basis. The physical characteristics ofthe solar generating system can include system size, orientation angles,equipment types, electrical characteristics, shading obstructions,latitude, longitude, and elevation.

The method can be used to forecast the net electrical energy imported orexported by a single facility with a solar generating system, from thesimulated solar energy production and the forecasted energy demand forthat facility.

The method can be used to forecast the total solar electrical energyproduction for a set of solar energy generating systems distributed overa region, by aggregating the forecast simulated energy production foreach solar generating system distributed over the region. Further, themethod can determine a net electrical energy production or consumptionfor a region with facilities that posses solar generating systems bysimulating net energy imported or exported by those facilities, incombination with the electrical energy consumption of facilities in theregion that do not posses solar energy generating systems. The methodcan forecast, on an hourly basis, the incident solar power on a targetsurface using the collected meteorological data, estimated irradiancelevels and physical characteristics of the target surface. The physicalcharacteristics of the target surface can include surface size,orientation angles, shading obstructions, latitude, longitude, andelevation.

The method can determine, for energy traders that have long-term energycontract obligations to serve loads, the short-term demand of theircustomers; some of which have solar generation capability within theirfacilities. The method can adjust a trader's short-term energy purchasesor sales, whether in a day-ahead, or a spot market. The energy tradercan use the process to estimate a net hourly energy production orconsumption of customers in the time period defined by the short-termmarket. Further, the method can insure or hedge energy sales orpurchases in a day-ahead market against shortfalls in the spot market.An hourly short-term energy futures market can be defined which consistsof options to buy or sell blocks of energy in the real time or spotmarket, at pre-agreed prices.

BRIEF DESCRIPTION OF DRAWINGS

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescription of the preferred embodiments of the invention, asillustrated in the accompanying drawings in which like referencedcharacters refer to the same parts throughout the different views.

FIG. 1A shows a system diagram for predicting electrical energyproduction for solar generating systems, one week in advance, on anhourly basis;

FIG. 1B shows a flow-diagram of the process of FIG. 1A;

FIG. 2A shows a system diagram for predicting the net electrical energyproduction or consumption for a single facility possessing a solargenerating system, one week in advance, on an hourly basis;

FIG. 2B shows a flow-diagram of the process of FIG. 2A;

FIG. 3A shows a system diagram for predicting electrical energyproduction for a set of solar generating systems, distributed over aregion, one week in advance, on an hourly basis;

FIG. 3B shows a flow-diagram of the process of FIG. 3A;

FIG. 4A shows a system diagram for predicting the net electrical energyproduction or consumption for a set of facilities possessing solargenerating systems, distributed over a region, one week in advance, onan hourly basis;

FIG. 4B shows a flow-diagram of the process of FIG. 4A;

FIG. 5A shows a system diagram for predicting the solar energy incidenton a surface, one week in advance, on an hourly basis;

FIG. 5B shows a flow-diagram of the process of FIG. 5A;

FIG. 6 shows a flow-diagram of a method to anticipate the short-termdemand for customers who have solar generation capability within theirfacilities; and

FIG. 7 shows a flow-diagram for a method for insuring or hedging energysales or purchases in the day-ahead, short-term market againstshortfalls in a spot market.

DETAILED DESCRIPTION OF THE INVENTION

A description of preferred embodiments of the invention follows.

In general as shown in FIG. 1A, one embodiment of the present inventionprovides a system 100 for predicting electrical energy output for asolar electric generating system (solar generator), a week in advance,in one-hour intervals. The solar generating system can be any type ofsolar generating system known in the art, such as a photovoltaicgeneration system. The system 100 includes a processor 110, a database120, a weather data module 130, a solar irradiance data module 140, asolar electric generating system characterization module 150, aconnection to an outside weather service provider 160, and an outputdevice 170. The database module 120, the weather data module 130, thesolar irradiance data module 140, and the solar electric generatingsystem characterization module 150 described further herein. Theconnection to an outside weather service provider 160 can be theinternet, direct dial-up or another connections means known in the art.The output device 170 can be a display, printer, or any other outputdevice know in the art.

The weather data module 130 includes a short-term weather forecastmodule 132 which collects meteorological one-week forecast data for ageographical location of the solar generator from the weather serviceprovider 160 and stores the data in the database 120. The solarirradiance data module 140 includes an estimation module 142 whichestimates type and intensity of solar radiation for the site fromselected meteorological parameters from the meteorological forecast dataand stores the data in the database 120. The solar electric generatingsystem characterization module 150 includes known characteristics of thesolar electric generating system and the characteristic data is storedthe data in the database 120. The system 100 combines the data from theweather data module 130, the solar irradiance data module 140, and thesolar electric generating system characterization module 150 andsimulates the energy output for the solar generator. The output isdisplayed on an output device 170. The output of the system 100 is anhourly estimate, for the week of the original weather forecast, of theelectrical energy production of the solar generator being modeled.

FIG. 1B shows a flow diagram of the process of FIG. 1A for predictingelectrical energy production by a solar electric generating system, onan hourly basis, one week in advance. The process can be implementedthrough the following steps:

-   -   1. Collect one-week weather forecast data 180 for a geographic        region of interest from a weather service provider, such as the        National Weather Service or a commercial alternative. Convert        the weather forecast data into a format required for use as        inputs in an irradiance model 182. The irradiance model 182 can        be MRM™ (Meteorological Radiation Model) or any other        appropriate irradiance model that produces estimates of solar        irradiance parameters. The solar irradiance parameters can        include global horizontal (GH), diffuse horizontal (DH), and        direct normal (DN) irradiance from the meteorological        parameters.    -   2. Insert the regional forecast meteorological data 180 into the        selected irradiance model 182 to estimate irradiance levels,        expressed in terms of GH, DH, and DN for the hours and the        region in question. Produce a combined database 184 for each        hour of the forecast week of irradiance parameters and        meteorological data. The combined database 184 is used in a        solar generator simulation software program 190, such as PV        Design Pro™.    -   3. Add the physical parameters 186 for the solar generating        system. The parameters can include system size, orientation        angles, equipment types, electrical characteristics, shading        obstructions, latitude, longitude, elevation, and other        features. The physical parameters 186 are added to the combined        database 184 to create a combined meteorological, irradiance and        solar generator database 188 needed to run the solar generator        simulation software program 190.    -   4. Run the simulation program 190 and output the electrical        system production for the systems being modeled. This result is        a database 192 containing the electrical output for each hour of        the forecast period for the system being evaluated.

FIG. 2A is another embodiment of the invention which provides a system200 for predicting the hourly net energy, relative to the point ofcommon connection with an electrical grid, that is either exported to orimported from the electrical grid for solar generating systems that arelocated within a facility that normally consumes electrical energy froman electrical grid. The system 200 includes the components of system 100of FIG. 1A and a load forecasting module 210. The load forecastingmodule 210 models the energy consumption of the facility for the sameperiod as the weather forecast used to predict the hourly solargeneration. The hourly forecast load for the facility is then deductedfrom the hourly forecast energy production. The result is an hourlyforecast of the net electrical energy produce or consumed by thefacility for the week in question.

FIG. 2B shows a flow diagram of the process of FIG. 2A for predicting onan hourly basis, one week in advance, the net electrical energy importedor exported from an electrical grid, by a facility that has a solargeneration system on the load side of the point of common connectionwith the electrical grid. The process can be implemented through thefollowing steps:

-   -   1. Collect one-week weather forecast data 180 for a geographic        region of interest from a weather service provider, such as the        National Weather Service or a commercial alternative. Convert        the weather forecast data into a format required for use as        inputs in an irradiance model 182. The irradiance model 182 can        be MRM™ (Meteorological Radiation Model) or any other        appropriate irradiance model that produces estimates of solar        irradiance parameters. The solar irradiance parameters can        include global horizontal (GH), diffuse horizontal (DH), and        direct normal (DN) irradiance from the meteorological        parameters.    -   2. Insert the regional forecast meteorological data 180 into the        selected irradiance model 182 to estimate irradiance levels,        expressed in terms of GH, DH, and DN for the hours and the        region in question. Produce a combined database 184 for each        hour of the forecast week of irradiance parameters and        meteorological data. The combined database 184 is used in a        solar generator simulation software program 190, such as PV        Design Pro™.    -   3. Add the physical parameters 186 for the solar generating        system. The parameters can include system size, orientation        angles, equipment types, electrical characteristics, shading        obstructions, latitude, longitude, elevation, and other        features. The physical parameters 186 are added to the combined        database 184 to create a combined meteorological, irradiance and        solar generator database 188 needed to run the solar generator        simulation software program 190.    -   4. Run the simulation program 190 and output the electrical        system production for the systems being modeled. This result is        a database 192 containing the electrical output for each hour of        the forecast period for the system being evaluated.    -   5. Run an energy demand forecasting simulation program, such as        CEDMS (Commercial Energy Demand Model System) or REDMS        (Residential Energy Demand Model System) for the facility that        includes the solar generating system that has just been modeled.        These models yield a demand profile for the facility being        considered that represents the electricity demand that these        customers would present in the absence of any customer-sited        generation.    -   6. Subtract the output of the demand forecasting simulation 220        from the output of the solar generator simulation program 192 to        produce a one-week forecast, on an hourly basis 240, of the net        electrical energy imported or exported from the grid.

FIG. 3A is another embodiment of the invention which provides a system300 for predicting the electrical energy output from a set of solarelectric generating system distributed over a region. The system 300includes the components of system 100 of FIG. 1A and a site aggregationmodule 310. The site aggregation module 310 repeats the process asdescribed in FIG. 1B until all of the solar electric generating systemsin the region have been modeled. With each iteration of the process, theenergy output of the solar electric generation system being evaluated issummed with that of those that have already been evaluated. The resultof this process is a one-week prediction, on an hourly basis, of thetotal energy production of the solar electric generating systemsdistributed over the region being evaluated.

FIG. 3B shows a flow diagram of the process of FIG. 3A for predictingthe electrical energy produced by a set of N solar electric generatingsystems, distributed over a region, on an hourly basis, one week inadvance. The process can be implemented through the following steps:

-   -   1. For a set of N solar generation systems distributed over a        region, set a variable X 1.    -   2. Collect one-week weather forecast data 320 for a geographic        region of interest from a weather service provider, such as the        National Weather Service or a commercial alternative, for the        sub-region or grid cell, closest to the solar generation system        corresponding to the location of site X. Convert the weather        forecast data into a format required for use as inputs in an        irradiance model 322. The irradiance model 322 can be MRM™        (Meteorological Radiation Model) or any other appropriate        irradiance model that produces estimates of solar irradiance        parameters. The solar irradiance parameters can include global        horizontal (GH), diffuse horizontal (DH), and direct normal (DN)        irradiance from the meteorological parameters.    -   3. Insert the regional forecast meteorological data 320 into the        selected irradiance model 322 to estimate irradiance levels,        expressed in terms of GH, DH, and DN for the hours and the        region in question. Produce a site X combined database 324 for        each hour of the forecast week of irradiance parameters and        meteorological data. The site X combined database 324 is used in        a solar generator simulation software program 330, such as PV        Design Pro™.    -   4. Add the physical parameters 326 unique to site X. The        parameters 326 can include system size, orientation angles,        equipment types, electrical characteristics, shading        obstructions, latitude, longitude, elevation, and other        features. The physical parameters 326 are added to the site X        combined database 324 to create a combined meteorological,        irradiance and solar generator database 328 needed to run the        solar generator simulation software program 330.    -   5. Run the simulation program 300 and output the electrical        system production for system X. Add this to the total for solar        generation systems 1 though X-1 and input to database 332.        Database 332 includes the electrical output for each hour of the        forecast period for the systems 1 through X-1. Next, increment        X.    -   6. Test 334 if X is greater that N. If X is less or equal to N,        return to step 1 and repeat the assessment the next site X. If X        is greater than N, then terminate the process. Database 332        becomes the final output database 336 which includes, on an        hourly basis, a one week forecast of the total electrical energy        production of N solar generation sites, distributed across the        region of interest.

FIG. 4A is another embodiment of the invention which provides a system400 for predicting the net energy for facilities in a region beingevaluated, exported to or imported from the region's electrical grid.The system 400 includes the components of system 300 of FIG. 3A and aload forecasting module 410. The forecasting module 410 adds the loadfor each facility which hosts a solar generation system, to theiterative process estimating the hourly electrical energy production fora set of solar generating system distributed over the region. With eachiteration of the process, the net electrical energy production orconsumption is summed with that of those that have already beenevaluated. The result of this process is a one-week prediction, on anhourly basis, of the net energy produced or consumed, by facilities thathost solar electric generating systems distributed over the region beingevaluated.

FIG. 4B shows a flow diagram of the process of FIG. 4A for predicting onan hourly basis, one week in advance, the net electrical energy importedor exported from an electrical grid, by a set of N facilities which areconnected to the electrical grid, and that have solar generation systemson the facility side of the point of common connection to the grid. Theprocess can be implemented through the following steps:

-   -   1. For a set of N facilities which are connected to an        electrical grid, and that have solar generation systems on their        side of the point of common connection, and are distributed over        a region, set a variable X=1.    -   2. Collect one-week weather forecast data 320 for a geographic        region of interest from a weather service provider, such as the        National Weather Service or a commercial alternative, for the        sub-region or grid cell, closest to the solar generation system        corresponding to the location of site X. Convert the weather        forecast data into a format required for use as inputs in an        irradiance model 322. The irradiance model 322 can be MRM™        (Meteorological Radiation Model) or any other appropriate        irradiance model that produces estimates of solar irradiance        parameters. The solar irradiance parameters can include global        horizontal (GH), diffuse horizontal (DH), and direct normal (DN)        irradiance from the meteorological parameters.    -   3. Insert the regional forecast meteorological data 320 into the        selected irradiance model 322 to estimate irradiance levels,        expressed in terms of GH, DH, and DN for the hours and the        region in question. Produce a site X combined database 324 for        each hour of the forecast week of irradiance parameters and        meteorological data. The site X combined database 324 is used in        a solar generator simulation software program 330, such as PV        Design Pro™.    -   4. Add the physical parameters 326 unique to site X. The        parameters 326 can include system size, orientation angles,        equipment types, electrical characteristics, shading        obstructions, latitude, longitude, elevation, and other        features. The physical parameters 326 are added to the site X        combined database 324 to create a combined meteorological,        irradiance and solar generator database 328 needed to run the        solar generator simulation software program 330.    -   5. Run the solar generator simulation program 330 using the site        X combined database 324 and output the electrical system energy        production for system X to produce a short-term energy        production database 420. The short-term energy production        database 420 includes the electrical output for each hour of the        forecast period for system X.    -   6. Run an energy demand forecasting simulation program 422, such        as CEDMS (Commercial Energy Demand Model System) or REDMS        (Residential Energy Demand Model System) for the facility that        includes the solar generating system X. These models yield a        demand profile for the facility being considered that represents        the electricity demand that these customers would present in the        absence of any customer-sited generation.    -   7. Subtract the output of the load forecasting simulation 422        from the output of the short-term energy production database        420, for site X, to produce a one-week forecast, on an hourly        basis, of the net electrical energy imported or exported from        the grid, for site X. Sum this value with the previous totals to        create a net production database 424 for X sites. Next,        increment X.    -   8. Test 334 if X is greater that N. If X is less or equal to N,        return to step 1 above and repeat the assessment the next        site X. If X is greater than N, then terminate the process.        Database 424 becomes the final output database 426 which        includes on an hourly basis, a one week forecast of the net        electrical energy imported or exported from the grid, from a set        of N facilities with solar generation, distributed across the        region of interest.

FIG. 5A is another embodiment of the invention which provides a system500 for predicting on an hourly basis, one week in advance, the incidentsolar power on any surface. The system 500 includes the components ofsystem 100 of FIG. 1A and a surface characterization module 510. Thesurface characterization module 510 includes a description of the size,orientation and environmental setting of the surface being assessed. Thecompleted database is then used as input to a model, in the form of asoftware program, that simulates solar power hitting surfaces on theearth. The output of the process is an hourly estimate, for the week ofthe original weather forecast, of the solar power hitting the surfacebeing modeled.

FIG. 5B shows a flow diagram of the process of FIG. 5A for predictingthe solar power incident on a surface, on an hourly basis, one week inadvance. The process can be implemented through the following steps:

-   -   1. Collect one-week weather forecast data 180 for a geographic        region of interest from a weather service provider, such as the        National Weather Service or a commercial alternative. Convert        the weather forecast data into a format required for use as        inputs in an irradiance model 182. The irradiance model 182 can        be MRM™ (Meteorological Radiation Model) or any other        appropriate irradiance model that produces estimates of solar        irradiance parameters. The solar irradiance parameters can        include global horizontal (GH), diffuse horizontal (DH), and        direct normal (DN) irradiance from the meteorological        parameters.    -   2. Insert the regional forecast meteorological data 180 into the        selected irradiance model 182 to estimate irradiance levels,        expressed in terms of GH, DH, and DN for the hours and the        region in question. Produce a combined database 184 for each        hour of the forecast week of irradiance parameters and        meteorological data. The combined database 184 is used in a        solar simulation software program 524, such as PV Design Pro™.    -   3. Add the physical parameters 520 for a target surface to be        accessed. The surface parameters can include surface size,        orientation angles, shading obstructions, latitude, longitude,        elevation, and other features. The physical parameters 520 are        added to the combined database 184 to create a combined        meteorological, irradiance and target surface database 522        needed to run the solar generator simulation software program        524.    -   4. Run the simulation program 524 and output the incident solar        power hitting the surface being modeled. This result is a        database 526 containing the solar power incident on the target        surface, for each hour of the forecast period.

FIG. 6 shows a flow diagram of another embodiment of the invention whichprovides a method to anticipate the short-term demand of an energytrader's customers, some of whom have solar generation capability withintheir facilities, for energy traders that have long-term energy contractobligations to serve loads. The method adjusts the trader's short-termenergy purchases or sales, whether in a day-ahead market or a spotmarket. The energy trader uses the process to estimate the net hourlyenergy production or consumption of customers in the time period definedby the short-term market. This estimate is subtracted from the long-termenergy contract amounts which the trader has purchased to meet loadobligations during the hours in question in the period defined by theshort-term markets. The difference between the forecast hourly estimateddemand (net production or consumption) and the energy amount that hasbeen purchased under long-term agreements, can be either sold as surplusor purchased to fill an anticipate shortfall. The process can beimplemented through the following steps:

-   -   1. A net energy 602 is calculated by an energy trader, using the        principals as described in FIGS. 4A and 4B. The net energy,        imported or exported from a set of facilities with which the        trader has a contractual obligation to supply a load, some of        which have solar generation systems on the facility side of the        point of common connection to the electrical grid, is        calculated, on an hourly basis, one week in advance.    -   2. A resulting energy 602 is calculated by the trader. The        trader subtracts the short-term estimated net energy 602 from        the amount of electrical energy purchased in long-term contracts        for the supply facilities 604. The calculation is done on an        hourly basis, one week in advance, within a geographic region.    -   3. A resulting difference 606 is an amount of energy that the        trader must purchase, or can offer for sale, in a short-term        market. The net purchase or sales 608 in the short-term market        is the difference between the trader's long-term contracted        energy purchases and the short-term energy purchases or sales        needed to meet his anticipated demand or load obligation.

FIG. 7 shows a flow diagram of another embodiment of the invention whichprovides a method for insuring or hedging energy sales or purchases in aone day-ahead, short-term market against shortfalls in a spot market. Anhourly short-term energy futures market is defined which consists ofoptions to buy or sell blocks of energy in the spot market, atpre-agreed prices. Buyers and sellers of options in this market will usethe performance history of the short-term net energy forecasts forregions with facilities, some of which have solar generation capabilitywithin their facilities, to assess the risk associated with purchases orsales of energy that are based upon those forecasts. Energy traders inthe short-term market buy energy because of an anticipated shortfall orsell energy because of an anticipated surplus in their long-termcontracts. Energy traders also purchase options to mitigate theirexposure for shortfalls in the spot markets. Energy traders purchaseoptions based upon their assessment of the accuracy of the forecastingtechnology, in combination with their tolerance for risk. A greaterliquidity is created in the market and permits a wider range of marketparticipants. The process can be implemented through the followingsteps:

-   -   1. In a deregulated electrical energy market that has        facilities, some of which have solar generation systems on the        facility side of the point of common connection to the        electrical grid, a petition is filed with the regulatory body        that oversees the market, requesting the creation of a product        category for a one-hour electrical energy future or option (if        this product does not already exist).    -   2. Qualifications for traders in one-hour energy futures are        proposed to the regulatory body that oversees the market (if        these qualifications do not already exist).    -   3. A venue and timetable for trading in one-hour energy futures        are proposed to the regulatory body that oversees the market (if        a venue and timetable does not already exist).    -   4. Once the regulatory body that oversees the market has        approved a one-hour electrical energy future or option product,        credentials for traders in one-hour electrical energy futures or        options, and a venue and time table for trading one-hour        electrical energy futures or options, options traders and energy        traders will assess the historical accuracy of estimates made by        energy traders of the purchases and sales of energy needed in        the short-term market, such as a day-ahead market, that makes up        the shortfall or surplus from their long-term contracts.    -   5. A historical performance or accuracy 702 of regional net        energy forecasts is determined using the principals as described        with reference to FIGS. 4A and 4B. The determination is for        markets that have facilities, some of which have solar        generation systems on the facility side of the point of common        connection to the electrical grid.    -   6. An estimation 704 of the difference between the maximum cost        of energy in a spot market and an energy trader's price of        energy for each hour during a forward spot market is calculated.    -   7. A risk determination 706 is made by the energy trader. The        energy trader calculates a tolerance for risk and the value of        mitigating the risk.    -   8. The value of options 708 is determined by energy traders and        option traders. Energy traders and options traders purchase and        sell options to buy energy, at a pre-agreed price, in a spot        market, during trading in the preceding day ahead market.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. A method of anticipating a short-term demand of an energy trader'scustomers, comprising: calculating a net electrical energy for a supplyfacility in a geographic region; determining a resulting electricalenergy from the net electrical energy and electrical energy purchasedfrom long-term contracts for the supply facilities; and bargaining ofelectrical energy in a short-term market based on the resultingelectrical energy.
 2. A method for hedging energy sales or purchases ina one day-ahead, short-term market against shortfalls in a spot market,comprising: determining a historical performance of a regional netenergy for a geographical region; estimating a difference between themaximum cost of energy in a spot market and an energy trader's price ofenergy for each hour during a forward spot market; determining a riskfactor associated with energy sales and purchases from the historicalperformance of a regional net energy and the estimated differencebetween the maximum cost of energy and the energy trader's price ofenergy; and purchasing options based on the determined risk.
 3. A methodof anticipating a short-term future electrical energy demand of anenergy trader's customers, the method comprising: calculating ashort-term future net electrical energy demand required to meet thedemand of customer facilities which have a solar energy generatingsystem or systems in a geographic region; determining a resultingdifference, expressed as a shortfall or surplus, between the short-termfuture net electrical energy demand and an amount of electrical energypurchased in long-term contracts for the supply of the customerfacilities; bargaining a buying price or a selling price for energy in ashort-term future or day-ahead market based on the shortfall or surplus.4. A method for hedging energy sales or purchases in a short-term futureor day-ahead market, the method comprising: determining an historicalperformance of a regional net energy forecasting methodology for afacility or facilities which have solar energy generating systems in ageographical region; estimating a difference between the maximum cost ofenergy in a spot market and an energy trader's minimum price of energyfor each hour bid in the short-term future or day-ahead market;determining a risk factor associated with energy sales or purchases fromthe historical performance and the estimated difference; and purchasingor selling options to buy energy at the previous day's day-ahead marketprice based on the determined risk factor.